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Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph's vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

Introduction This survey of international experts in obesity management was conducted to achieve consensus on standardized definitions and to identify areas of consensus and non-consensus in metabolic bariatric surgery (MBS) to assist in an algorithm of clinical practice guidelines for the management of obesity. Methods A three-round Delphi survey with 136 statements was conducted by 43 experts in obesity management comprising 26 bariatric surgeons, 4 endoscopists, 8 endocrinologists, 2 nutritionists, 2 counsellors, an internist, and a pediatrician spanning six continents over a 2-day meeting in Hamburg, Germany. To reduce bias, voting was unanimous, and the statements were neither favorable nor unfavorable to the issue voted or evenly balanced between favorable and unfavorable. Consensus was defined as ≥ 70% inter-voter agreement. Results Consensus was reached on all 15 essential definitional and reporting statements, including initial suboptimal clinical response, baseline weight, recurrent weight gain, conversion, and revision surgery. Consensus was reached on 95/121 statements on the type of surgical procedures favoring Roux-en-Y gastric bypass, sleeve gastrectomy, and endoscopic sleeve gastroplasty. Moderate consensus was reached for sleeve gastrectomy single-anastomosis duodenoileostomy and none on the role of intra-gastric balloons. Consensus was reached for MBS in patients > 65 and < 18 years old, with a BMI > 50 kg/m 2 , and with various obesity-related complications such as type 2 diabetes, liver, and kidney disease. Conclusions In this survey of 43 multi-disciplinary experts, consensus was reached on standardized definitions and reporting standards applicable to the whole medical community. An algorithm for treating patients with obesity was explored utilizing a thoughtful multimodal approach. Graphical Abstract

This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%) . Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.

Optical microscopy techniques for plant inspection benefit from the fact that at least one of the multiple properties of light (intensity, phase, wavelength, polarization) may be modified by vegetal tissues. Paradoxically, polarimetric microscopy although being a mature technique in biophotonics, is not so commonly used in botany. Importantly, only specific polarimetric observables, as birefringence or dichroism, have some presence in botany studies, and other relevant metrics, as those based on depolarization, are underused. We present a versatile method, based on a representative selection of polarimetric observables, to obtain and to analyse images of plants which bring significant information about their structure and/or the spatial organization of their constituents (cells, organelles, among other structures). We provide a thorough analysis of polarimetric microscopy images of sections of plant leaves which are compared with those obtained by other commonly used microscopy techniques in plant biology. Our results show the interest of polarimetric microscopy for plant inspection, as it is non-destructive technique, highly competitive in economical and time consumption, and providing advantages compared to standard non-polarizing techniques.

Expanding the mass of pancreatic insulin-producing beta cells through re-activation of beta cell replication has been proposed as a therapy to prevent or delay the appearance of diabetes. Pancreatic beta cells exhibit an age-dependent decrease in their proliferative activity, partly related to changes in the systemic environment. Here we report the identification of CCN4/Wisp1 as a circulating factor more abundant in pre-weaning than in adult mice. We show that Wisp1 promotes endogenous and transplanted adult beta cell proliferation in vivo. We validate these findings using isolated mouse and human islets and find that the beta cell trophic effect of Wisp1 is dependent on Akt signaling. In summary, our study reveals the role of Wisp1 as an inducer of beta cell replication, supporting the idea that the use of young blood factors may be a useful strategy to expand adult beta cell mass. The proliferation of pancreatic beta cells decreases with age, partly due to systemic changes. Here the authors identify Wisp1 as a circulating factor enriched in young serum that induces adult beta cell proliferation, supporting the idea that young blood factors may be useful to expand beta cell mass.

Fire is a major agent involved in landscape transformation and an indirect cause of changes in species composition. Responses to fire may vary greatly depending on life histories and functional traits of species. We have examined the taxonomic and functional responses to fire of eight taxonomic animal groups displaying a gradient of dietary and mobility patterns: Gastropoda, Heteroptera, Formicidae, Coleoptera, Araneae, Orthoptera, Reptilia and Aves. The fieldwork was conducted in a Mediterranean protected area on 3 sites (one unburnt and two burnt with different postfire management practices) with five replicates per site. We collected information from 4606 specimens from 274 animal species. Similarity in species composition and abundance between areas was measured by the Bray-Curtis index and ANOSIM, and comparisons between animal and plant responses by Mantel tests. We analyze whether groups with the highest percentage of omnivorous species, these species being more generalist in their dietary habits, show weak responses to fire (i.e. more similarity between burnt and unburnt areas), and independent responses to changes in vegetation. We also explore how mobility, i.e. dispersal ability, influences responses to fire. Our results demonstrate that differences in species composition and abundance between burnt and unburnt areas differed among groups. We found a tendency towards presenting lower differences between areas for groups with higher percentages of omnivorous species. Moreover, taxa with a higher percentage of omnivorous species had significantly more independent responses of changes in vegetation. High- (e.g. Aves) and low-mobility (e.g. Gastropoda) groups had the strongest responses to fire (higher R scores of the ANOSIM); however, we failed to find a significant general pattern with all the groups according to their mobility. Our results partially support the idea that functional traits underlie the response of organisms to environmental changes caused by fire.

BackgroundSarcopenia pre-dating bariatric surgery (BS) has been suggested as concern for the use of BS in older-adults with morbid obesity.ObjectiveTo evaluate the impact of age on the prevalence of sarcopenic obesity (SO) in BS-candidates.MethodsCross-sectional study including 1370 consecutive BS-candidates aged ≥18, and grouped according to age: 18–39 (reference group), 40–49, 50–59 and ≥ 60 years. From body composition analysis data obtained using bioelectrical impedance, skeletal muscle mass (SMM), SMM index (SMMI=SMM/height2), and percentage of SMM (%SMM = SMM/BW*100) were calculated. Class I or class II SO was adjudicated, respectively, when a value between > − 1 and − 2, or > −2 standard deviations from the regression line from the gender-specific distribution of the relationship between BMI and SMMI or the %SMM in the reference group was encountered.ResultsAccording to the SMMI distribution, prevalence of class I and class II SO in the whole cohort was respectively 16.4% and 4.6%. SO was more prevalent in females (p < 0.005). Proportion of subjects with SO positively correlated with older age category in females (Tau-c = 0.149, p < 0.001) but not in males. In females aged ≥60, class I SO was present in 29.1%, and class II in 12.8%. Similar results were obtained when %SMM was used (Cohen’s k-coefficient = 0.886, p < 0.001). Age and female gender were identified as independent preditors of SO, whereas CRP or the presence of obesity-associated comorbidities were not.ConclusionAge is a risk factor for SO in BS-candidates. SO is fairly common in female subjects aged >60 years that are candidates to BS.

The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R 2 were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error ≥ 401.40 and R 2 ≤ 0.62 in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC ≥ 0.61 and PR AUC ≥ 0.42), especially for class 1. These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.

Objectives To determine how the COVID-19 lockdown influenced the lifestyle, eating behavior, use of substances, mental health, and weight in patients who had undergone bariatric surgery (BS) and explore the self-perception of one’s own health and fears related to COVID-19. Methods We performed a cross-sectional exploratory study in obesity patients who had undergone BS surgery > 1 year previously in a university hospital. Assessment was performed 40 days after initiating lockdown and included 2 periods: from April 24 until May 8 and during the initial de-escalation period: from May 9 until 22, 2020. A structured telephone interview and an online survey were administered. Results One hundred eighty-eight patients were interviewed; 156 also responded to the online survey (77% females, mean age 53.46 ± 10.48 years, mean follow-up 5.71 ± 4.30 years). Dietary habits were affected in 72% of the participants, with 15% reporting better diet planning; 83.5% reported having more sedentary behaviors; 27% and 36% showed depression and anxiety, respectively; and 45% of participants reported bad sleep quality. In relation to changes in the use of any substance, the use increased in the majority of patients who were previously users. Self-perception of one’s own health and fears related to COVID-19 were only moderate. Finally, emotional eating and time since BS were statistically significant risk factors for predicting weight gain. Conclusions Lockdown during COVID-19 pandemic negatively influenced the lifestyle, mental health, substance use, and weight in BS patients. These alterations were somewhat similar to those observed in the general population but more severe and with important clinical implications. Graphical abstract

The digital landscape has become a vital platform for public health discourse, particularly concerning important topics like organ donation. With a global rise in organ transplant needs, fostering public understanding and positive attitudes toward organ donation is critical. Social media platforms, such as X, contain conversations from the public, and key stakeholders maintain an active presence on the platform. The goal is to develop insights into organ donation discussions on a popular social media platform (X) and understand the context in which users discussed organ donation advocacy. We investigate the influence of prominent profiles on X and meta-level accounts, including those seeking health information. We use credibility theory to explore the construction and impact of credibility within social media contexts in organ donation discussions. Data were retrieved from X between October 2023 and May 2024, covering a 7-month period. The study was able to retrieve a dataset with 20,124 unique users and 33,830 posts. The posts were analyzed using social network analysis and qualitative thematic analysis. NodeXL Pro was used to retrieve and analyze the data, and a network visualization was created by drawing upon the Clauset-Newman-Moore cluster algorithm and the Harel-Koren Fast Multiscale layout algorithm. This analysis reveals an \"elite tier\" shaping the conversation, with themes reflecting existing societal sensitivities around organ donation. We demonstrate how prominent social media profiles act as information intermediaries, navigating the tension between open dialogue and negative perceptions. We use our findings, social credibility theory, and review of existing literature to develop the OrgReach Social Media Marketing Strategy for Organ Donation Awareness. The OrgReach strategy developed is based on 5 C's (Create, Connect, Collaborate, Correct, and Curate), 2 A's (Access and Analyse), and 3 R's (Recognize, Respond, and Reevaluate). The study highlights the crucial role of analyzing social media data by drawing upon social networks and topic analysis to understand influence and network communication patterns. By doing so, the study proposes the OrgReach strategy that can feed into the marketing strategies for organ donation outreach and awareness.